neuralib.scanner.lsm.LSMConfocalScanner

final class neuralib.scanner.lsm.LSMConfocalScanner[source]

Bases: AbstractConfocalScanner

lsm confocal image data

lsmfile: ndarray

lsm image array

meta: dict[str, Any]

metadata dict

classmethod load(filepath)[source]
Parameters:

filepath (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader)

classmethod get_meta(filepath)[source]
Parameters:

filepath (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader)

Return type:

dict[str, Any]

get_dim_code()[source]

get the DimCode

Dimension parameters (DimCode):

V - view

H - phase

I - illumination

S - scene

R - rotation

T - time

C - channel

Z - z plane (height)

M - mosaic tile, mosaic images only

Y - image height

X - image width

A - samples, BGR/RGB images only

Return type:

DIMCODE

property width: dict[int, int]

X

property height: dict[int, int]

Y

property n_channels: dict[int, int]

number of fluorescence channels. C

property n_zstacks: dict[int, int]

number of stacks in z axis. Z

get_image(channel, depth=None, zproj_type='max', norm=True)[source]
Parameters:

  • channel (int)

  • depth (int | slice | ndarray | None)

  • zproj_type (Literal['avg', 'max', 'min', 'std', 'median'])

  • norm (bool)

Returns:

Return type:

ndarray

imshow(channel, depth=None, add_scale_bar=True, zproj_type='max', norm=True, output=None)[source]
Parameters:

  • channel (int)

  • depth (int | slice | ndarray | None)

  • add_scale_bar (bool)

  • zproj_type (Literal['avg', 'max', 'min', 'std', 'median'])

  • norm (bool)

  • output (str | Path | bytes | BinaryIO | BufferedIOBase | BufferedReader | None)

Returns:

__init__(lsmfile, meta)
Parameters:

  • lsmfile (ndarray)

  • meta (dict[str, Any])

Return type:

None

get_pixel2mm_factor()
plot_merge_channel()[source]
n_scenes: int

positions scan